Data Science vs Software Engineering: Which One Offers Better Career Perks in 2022
Software Development is one of the most popular employment fields. For its creative working outlet, many computer geeks want to set a career path in software engineering.
The same goes for Data Science, but the core difference is that this field is new in the IT industry. Some people are pretty enthusiastic, while the majority are unfamiliar and probably anxious to pursue a profession in Data Science.
But, according to Linkedin, Data Science had moderate job growth of 650% since 2012. Yep, it is becoming popular like Software Development and may even surpass in the future.
If you are confused about Data Science vs Software Engineering, continue reading. The contents of this article will surely help you overcome your hesitation and choose the right one.
Who is a Software Engineer?
Software developers build and design software in the easiest way for the general users. Their main priority is making a system that is the most comfortable software in the current marketplace. Software engineers are experts in multiple coding languages and work in groups to finish big programming projects.
Moreover, their work may also consist of-
- Learning a new language
- Modification of existing software
- Bug fixing
- Frame building
And various other activities throughout the software development life cycle.
Software Engineering Responsibilities in an IT Company
In a working environment, the work of a software developer can significantly impact virtual product building more than any other engineering post in the IT sector.
A slight error in the code will create a disaster, so they have to spend hours on tests and debugging. Moreover, every software developer manager has tons of duties because the entire team relies on their directions.
Most responsibilities will be related to completing any task in a given time. A software developer manager may have to adjust their schedule and stay flexible even on weekends to prepare for any assigned work. A DevOps can work under all pressure. To be a DevOps engineer, you have to control development and operational sectors.
Some of the crucial software developer responsibilities include,
- Research and manage new software programs.
- Bug and other issue identification in software.
- Apt of writing precise and efficient code.
- Prioritize user feedback before implementing any change.
- Competent in reviewing code.
- Be an expert in writing technical documentation.
- Suggest and implement updates over a specific program.
- Should handle software tools, processes, and metrics.
- Collaboration with developers from other branches.
- Expert in maintaining every company lodged software to address company and customer concerns.
- Check and improve all sorts of software securities in every update to prevent malicious attacks.
Who is a Data Scientist?
In simple terms, data science is analyzing, researching, recognizing, and collecting data. Data science can also be related to data mining, AI technology, Causal Reasoning, and statistics.
A professional data scientist performs many analyses and gathers crucial insights from a pile of collected data for the company. Moreover, they have to extract knowledge from unstructured and structured data and turn them into meaningful insights.
The collection process in data science is done by artificial intelligence (AI) systems and stored in massive data centers. The work of data science is to apply machine learning models and figure out user-favored trends.
They may also have to develop their own models to monitor numbers-based data such as stock.
Nowadays, any company’s success heavily depends on how well it processes its data. Sometimes trivial information can turn an inefficient product into an effective marketing tool, so data science has become an essential working field in any corporate office.
Data Scientist Job Responsibilities
The primary task of a data scientist is to process all inside and outside source data for the company. Data scientists also have to attend meetings with their superiors and inform their work.
Moreover, many data scientists and business stakeholders work together to achieve company goals and understand crucial statistics. They often develop data models to process and create algorithms to predict the result for any future business operations outcome.
Data scientists have to process the raw data and discover any business opportunities. Their work should help the managers make decisions efficiently based on current trends, interests, and statistics.
Some of the crucial data scientist responsibilities include,
- Sorting and specifying other relevant data sources for business.
- Organizing all sorts of structured-unstructured data.
- Developing algorithms based on machine learning.
- Creating a lost data list.
- Verifying any unverified data sources.
- Assembling predictive models to know any outcome of a project.
- Check for data quality and remove any unnecessary data.
- Have to be skilled in inspecting large data logs and discovering potential trends.
- Manage database and other data infrastructures.
- Have expertise in data visualization for presentations.
- Cooperate with the development and engineering team.
- Perform analytics and set up surveys to collect valuable data.
- Suggest any required adjustment to the developer team based on customer feedback.
- Analyses results and improves the future outcome of any operation.
- Run all sorts of analysis models in production by cooperating engineers and software developers.
Data Science vs Software Engineering: 9 Key Differences
Software engineers are the building blocks of a company. But, products developed by software engineers don’t hold any value if prospects don’t purchase them and become genuine customers. Research is needed to create customer-desired products.
So, data scientists turn software engineers’ hard-significant but inefficient work into efficient work through data research. Below, we have gathered 9 primary differences between data science and software engineering that will help you further understand these two fields.
1. Difference in Working Methodology
Software engineers use the SDLC (Software Development Life Cycle) to use their data to establish a systematic process for developing excellent software and maintaining them later.
In the SDLC methodology, software engineers need to follow some specific phases of development,
- Design and model development.
- Final product development.
- Deployment and update.
The SDLC methodology originated in the 1960s to build enormous business systems and aid in functional product processing. Moreover, it had used for measuring and updating business development processes.
One of the benefits of the SDLC methodology is that it allows companies to maintain efficiency for a long time, deliver quality products, make business cost-effective, and meet high demand.
A data scientist uses ETL (Extract, Transform, & Load) methodology to process any data type. The main objective of this methodology is to extract data from numerous sources and combine them.
Later, load this information in one single database or other data holding facility. If any data scientist wants to perform analytics or research, then he can use the processed data from these storing areas.
ETL is the core methodology of data science. And after the rise in popularity of databases in the 1970s, it became the most preferred methodology for storing data.
2. Dissimilarities in Programming Language
Software engineers have to write code constantly and deliver updates frequently. So, they use programming languages that save them time and support them write code faster.
Some of the most used programming languages by software engineers are,
- and Pearl.
Most data scientists have to run analytics and research to process massive data chunks. So, data scientists use a programming language that can run these operations for them.
However, to create machine learning algorithms, data scientists may have to rely on programming languages used by software engineers. The best programming languages used to run analytics, data visualization, and deliver statistical reports are,
- R-Programming language.
- And MATLAB.
3. Specific Tools in Both Areas
Tools like code editors and task management dashboards can help software engineers focus on work. Plus, they can make the task easier and assist in finishing their work faster. Moreover, software engineers also use tools to complete the Orchestration.
Orchestration is a software development methodology that focuses on automation, management, related application, computer system coordination, management, and service.
Most software engineers use tools such as,
- Decompiler (binary decoder).
- Collaborator (code review tool).
- Jenkins (open-source automation server).
- Vaadin (open-source web platform for Java)
- Make (system-agnostic software).
- Eclipse IDEs (IDE for Java Development).
- Docker (open-source containerization platform).
Most data scientists have to rely on statistics and probability reports for their work. Without help from tools, acquiring massive information for a giant tech company is nearly impossible.
So, any tool that can help them analyze data, extract information, and help visualize data with elements can be compared to data science tools. However, data processing tools can be pretty expensive and may cost you thousands for subscriptions.
Here are some of the best data science tools,
- Ahrefs (Key-word researching tool).
- Apache Spark (analytical engine).
- BigML (Machine learning tool).
- Excel (Microsoft data analytic tool).
- Tableau (data visualization tool).
- NLTK (Natural Language Processing).
4. Impact on Software Industry
In any tech company, software engineering is the physical foundation. People working in this field develop information-based applications.
Moreover, these applications will be bug-free and receive updates making them valuable for the company. No tech company is a company without a genuine product or in-development product.
So, if we think this way, the software engineering post is the most valuable in the tech industry and has the highest impact than any other job. Software engineering has spun the wheel of the technological revolution for software development.
The term “Data Science” was used as a second name for computer science. However, after 1996, IFCS (International Federation of Classification Societies) changed it and thought of a different meaning for the name.
In the 21st, companies utilize data to solve problems, control the market, and protect-manage their own data.
Moreover, by understanding data correctly, tech companies can develop products based on people’s interests. Data science is helping IT companies achieve this goal. The impact of data science is immense and will keep rising in the future.
5. Career in Data Science and Software Engineering
Software engineering has a straightforward career path and fair job opportunities. There are many software engineering positions that you can try out.
Moreover, there is always a learning environment and new possibilities to explore. According to career profiles, a software engineer will have to work 40-50 hours a week, which is pretty decent.
You can enter coding boot camps to increase your programming skills. However, With the necessary aptitudes, experience, and required etiquette, you can get a job as a software engineer without even any academic education.
According to an article in Harvard Business Review magazine in 2012, data science is the sexiest job in the 21st century. But, being a new IT field, data science is not preferred among techies.
Moreover, getting a job in data science is challenging because most companies value a higher level of education (we are talking about a master’s or doctorate in computer science or economics).
But, data science boot camps have made the career path less education reliant.
6. Must-Have Expertise
Software development requires expertise in programming languages, configuration tools, programming structures, OOD (Object-Oriented Design), and problem-solving. Moreover, a software engineer also needs logical thinking to cope with any change. Different types of developers need agilities according to their working area.
A data scientist will need good experience in machine learning and control mechanisms.
For the research and analysis part, a data scientist needs an expert understanding of analytical and database tools. They may also have to possess domain knowledge and represent any operation to their superiors.
Apart from professional skills, a data scientist must have soft skills to maintain a peaceful working environment and collaborate with everyone.
7. Educational Background
Skills and experience are prioritized more than education in the software engineering field. In most cases, candidates on a job interview lack general programming knowledge but may have good results in the academic area. That’s why there are so many applicants but less employment.
Almost 90% of software developers say that their academic knowledge is useless in the workplace. If you look at the big picture, most independent developers are college dropouts.
However, a diploma is still a priority for recruiters at most large IT firms to look for when hiring a programmer. So, we can’t deny a computer science degree education because you will get a basic understanding of all the crucial knowledge about programming language and related subjects.
Later, you can rely on coding boot camps and online programming courses to learn the lessons you didn’t know in your university classes.
As we said earlier, academic education is a must to get a job as a data scientist. Here are some crucial educational backgrounds a data scientist needs,
- Must have a 4-year computer science bachelor’s degree.
- A Ph.D. in similar data science subjects (mandatory).
- Some data science jobs require previous data engineer working experience.
- Must require intermediate-level knowledge of Machine Learning algorithms.
- Must have a background working in data visualization and cloud infrastructure.
8. Salary & Compensation
The salary in an IT company mainly depends on the software engineer type.
The average salary for a software engineer in the USA is $120,932 yearly. If you extract it with 12, you will get $8,562 monthly.– Indeed.com
However, this is not final. The salary changes depend on some crucial factors such as,
- Yearly promotions.
- Change of position.
- Change in payscale.
The highest-paid engineering job in an IT infrastructure is the Chief Technology Officer, who gets paid $157,005 yearly and $13,083 monthly. A Chief Technology Officer has many responsibilities and is considered the second highest-ranked software development position in a tech company. DevOps is also a highly paid position in software engineering.
Most software engineering companies have maybe 2 to 3 levels of data science job titles, such as junior data scientist and senior data scientist.
The highest data science job position is the chief data officer.
Here is a list of salaries based on the job position of a data scientist in the USA,
|Job Positions||Salary (Yearly)|
|1) Junior Data Scientist||$74,596|
|2) Lead Data Scientist||$115,571|
|3) Senior Data Scientist||$143,144|
|4) Machine Learning Engineer||$145,297|
|5) Chief Data Officer||$178,240|
However, like in software engineering, the salary is not fixed, and the change depends on the same factors.
9. Approach Towards Solving Problems
Problems in software engineering are more like coding problems that require specific steps and logical thinking to solve.
So, when software engineers solve problems, they use different types of frameworks, algorithms, techniques, and methodologies to reach the correct solution.
We can take the Waterfall model as a brilliant technique; it’s an SDLC approach that displays required software development steps in a linear sequential flow instead of a straightforward process flow. This model instructs developers to test and review every step in the SDLC methodology.
Some popular SDLC models include Spiral, Agile, Iterative, and V-shape.
Problem-solving in data science is completely different from software engineering. In software engineering, irrelevant data from the current programming problem is unnecessary. But, in data science, you will have to work with information outside the present issue to find a solution.
Moreover, data scientists use many specific processes and are more of a process-oriented field.
Some fascinating data science storage models include,
- Data Lake Architecture.
- Double layer.
- Amazon s3.
- Oracle Cloud.
What Are Some of the Similarities Between Data Science and Software Engineering?
Just like differences, data science and software engineering have some significant similarities too-
- Employees from both sectors coordinate with experts to complete any project.
- Both require programming language skills.
- AI (Artificial Intelligence) is a field that needs data science and software engineering knowledge.
- Both career fields have a high entry-level career demand.
Which Field Would Be Better for You as a Profession?
In the 21st century, picking a career in the data science or software engineering field is always the right decision. Because both these fields are necessary for developing people-favored software and applications.
Software engineers can create marketable products using models, data statistics, and customer research results provided by data scientists. Better communication between both groups can help in developing the perfect software.
Data science and software engineering both fields have great careers. So, choosing one between these two will totally depend on you.
If you have expertise in machine learning, statistics, and mathematics, picking data science is better. Prepare yourself to do a lot of research and new ways to gather crucial information.
But, if you want to be more hard-working and love to take on challenges, then software engineering is the best choice. Prepare yourself to write and review dozens of codes.
Acquire the Right Set of Skills to Become a Desirable Data Scientist or Software Engineer
After completing the necessary academic education, you can do many computer sciences and programming language-related courses such as Information Security, Software Development, or Database Administration.
Later, you can start preparing yourself for coding boot camps and online courses. The more you know, the easier you can answer your interview questions.
We hope the information in this article can help you select a suitable career. Let us know your thoughts on the data science vs software engineering topic, and ask any related questions.
Have a wonderful day!
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